Numpy is a popular library that allows you to manipulate any array easily. It speeds up the computational works in an efficient way. It has many functions that do so. **Numpy Logspace** is one of them. In this entire tutorial, you will know how to implement this method with various examples.

Before going to the examples part, first, learn the syntax of the * numpy.logspace()* method.

`numpy.logspace(start, stop, num=50, endpoint=True, base=10.0, dtype=None, axis=0)`

**Below are the explanation of the parameters.**

**start:***It is the starting value of the sequence.*

**stop:***Final value of the sequence.*

**num:***Number of samples to generate. The default value is 50.*

**endpoint:***It is true or False. If it is true then the last element is included in the sequence. If not then not. The default value is true.*

**base:***It is the stepsize. The default value is 10.*

**dtype :***Type of the output array. *

**axis :***The axis in the result to store the samples.*

All these are simple explanations of the parameters. If you want to know more then you can visit the Official Numpy Documentation.

## Examples on Implementation of Numpy logspace

### Example 1: Simple Implementation of Numpy logspace

In this example, I will use only three parameters * start, stop, and num*. Please keep in mind that the method uses a logarithmic scale. That’s why choose a low number. Execute the code below.

```
import numpy as np
log_array = np.logspace(start=2,stop=3,num=5)
print(log_array)
```

You can see here I am using the start value of 2 and stop value 3. Num =5 will generate 5 elements.

**Output**

### Example 2: Use of Numpy logspace with base

The above example was using three parameters. But in this example, I am also using the base parameters. You can compare it as the base of the logarithmic scale. The default is 10. But here I am using the base as 2.

Execute the lines of code and see the output.

```
import numpy as np
log_array = np.logspace(start=2,stop=3,num=5,base=2)
print(log_array)
```

**Output**

### Example 3: Changing the type of the output Array

In all the above examples, you can see that you are getting the output as float type. Suppose I want to get all the elements in the output as int. Then I have to use the * dtype =int* parameter. Run the following lines of code and see the output.

```
import numpy as np
log_array = np.logspace(start=2,stop=3,num=5,base=2,dtype=int)
print(log_array)
```

**Output**

You can see in the output I am getting all the elements of integer type.

## Conclusion

These are examples We have compiled for you. Numpy logspace() can be compared with linspace(). The difference is only that logspace calculates the log of each value and then return the results. I hope that you understand the implementation of * logspace() *method. If you have any queries then you can contact us for more information.

#### Join our list

Subscribe to our mailing list and get interesting stuff and updates to your email inbox.